S59. A CLUSTER-ANALYTIC APPROACH TO EXAMINING MOTIVATION SYSTEM IMPAIRMENTS IN SCHIZOPHRENIA AND MAJOR DEPRESSIVE DISORDER
Bibliographic record
Abstract
Motivation deficits have been linked to poor functional outcomes in schizophrenia (SZ) and major depressive disorder (MDD) and represent an unmet therapeutic need. Recent conceptualizations of the motivation system have outlined five inter-related reward processes, whereby (1) reward responsiveness (i.e. “liking”) and (2) reward prediction (i.e. “wanting”, established through appropriate reward learning) converge to inform both (3) reward valuation and (4) effort valuation which is associated with a cost-benefit analysis, followed by (5) the development and execution of an action plan to achieve the desired outcome. The inclusion of approach motivation within the RDoC Positive Valence System further underscores its importance as a pervasive symptom that cuts across traditional diagnostic boundaries. Previous investigations, however, have typically only focused on isolated reward processes, often within single diagnostic groups. Thus, in line with emerging dimensional approaches to examining psychopathology, the present study sought to utilize a cluster-analytic approach to objectively evaluate the multiple facets of the motivation system concurrently across SZ, MDD, and healthy control (HC) participants. The study sample consisted of 39 SZ, 38 MDD, and 39 HC participants. Participants were administered a series of assessments to evaluate symptom severity and cognitive functioning. Discrete facets of the motivation system were measured using an extensive battery of objective computerized tasks. Variables of interest were extracted from each task and entered into a principal components analysis in order to explore the factor structure of the motivation framework. Factor scores were subsequently applied to K-means cluster analysis to identify subgroups of individuals with similar motivation profiles. Principal components analysis revealed five distinct motivation factors: hedonic capacity, reward expectancy and learning, cost-benefit decision-making, goal-directed decision-making, and effort expenditure. K-means clustering identified two distinct subgroups of individuals based on their motivation task performance. The first cluster demonstrated impaired hedonic capacity (t(114)=-3.7, p<.001), whereas the second cluster was characterized by impairments in cost-benefit decision-making (t(114)=5.9, p<.001), goal-directed decision-making (t(114)=7.3, p<.001), and effort expenditure (t(114)=6.3, p<.001). Although clusters did not differ in symptom severity, the second cluster was associated with significantly greater cognitive impairments (t(114)=6.4, p<.001). Importantly, all diagnostic groups were well represented in each cluster, though with significantly different distributions. Our dimensional investigation revealed a multi-faceted motivation framework comprised of five distinct components. The emergence of two unique motivation performance profiles highlights the extensive heterogeneity of clinical amotivation and its dimensionality across disorders. Further, the pattern of motivation impairments within these profiles raises the possibility of distinct underlying neural substrates, with implications for specific therapeutic targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".